Machine vision systems are used in a wide range of manufacturing processes. Among other uses, machine vision is employed to ensure quality and to assist in the constructing various articles and devices. A common use for vision systems in industrial applications is in association with an inspection or assembly location on a moving production line. Images of objects are acquired as they pass under the vision system camera's field of view. Based upon image data acquired, the vision system can decide whether there is a flaw in the object, determine its alignment for further processes, operate a robot manipulator, identify and sort the object into an appropriate bin, or perform other automated tasks.
Another common vision system task is the measurement, inspection, alignment of objects and/or decoding of symbology (e.g. bar codes), which are used in a wide range of applications and industries. These systems are typically based around the use of an image sensor, which acquires images (typically grayscale or color, and in one, two or three dimensions) of the subject or object, and processes these acquired images using an on-board or interconnected vision system processor. The processor generally includes both processing hardware and non-transitory computer-readable program instructions that perform one or more vision system processes to generate a desired output based upon the image's processed information. This image information is typically provided within an array of image pixels each having various colors and/or intensities. In the example of a symbology (barcode of “ID”) reader, the user or automated process acquires an image of an object that is believed to contain one or more IDs/codes. The image is processed to identify ID/code features, which are then decoded by a decoding process and/or processor obtain the inherent alphanumeric data represented by the code.
Often, a vision system camera includes an internal processor and other components that allow it to act as a standalone unit, providing a desired output data (e.g. decoded ID/code information, alignment and pose information, tracking, defect-finding and the like) to a downstream process, such as an inventory tracking computer system or manufacturing control device. Alternatively, vision system tasks can be performed using appropriate tools and processes running on a remote processor (e.g. a standalone PC or server), and the camera assembly operates primarily to capture and transfer image data to the remote processor. The camera assembly also typically contains a lens mount, such as the commonly used C-mount, that is capable of receiving a variety of lens configurations so that it can be adapted to the specific vision system task.
In imaging IDs/codes and other visual information on an object (often passing down a conveyor line), the focal distance of the object of interest often changes—for example where differing sized boxes pass under a vision system camera. One approach to resolving the correct focal distance to capture the clearest, sharpest image of region of interest on an object in a conventional camera using, for example, a standard lens that projects directly onto a sensor pixel array is to employ a lens assembly with automatic focusing (auto-focus) function. A variety of auto-focus assemblies are presently available. Some electro-mechanically alter the distance between lenses to accommodate differing focal distance. Alternatively, an auto-focus lens can be facilitated by a so-called liquid lens assembly. One form of liquid lens uses two iso-density liquids—oil is an insulator while water is a conductor. The variation of voltage passed through the lens by surrounding circuitry leads to a change of curvature of the liquid-liquid interface, which in turn leads to a change of the focal length of the lens. Some significant advantages in the use of a liquid lens are the lens' ruggedness (it is free of mechanical moving parts), its fast response times, its relatively good optical quality, and its low power consumption and size. Relative to other autofocus mechanisms, the liquid lens also exhibits extremely fast response times.
Nevertheless, a liquid lens, like other auto-focus mechanisms, exhibits a delay in changing focal distance. This can be disadvantageous where the object is passing through the imaged scene at relative speed. The lens may lack sufficient time to refocus onto the next object as it arrives at the scene. Likewise, various regions of interest on a single object may be located at differing focal distances, causing only some regions to be clearly imaged, and potentially missing important visual information.
One option to enable an object to be imaged over a longer focal range to is to employ a camera assembly having optics characterized by extended depth of field (EDOF), or “full focus”, characteristics. In such cameras the focal distance is typically short and thereafter extends to infinity. Often such systems employ a small aperture that receives reduced light. Such systems are often inefficient for use in a vision system application, particularly where object illumination may be unpredictable or limited. In addition acquired images may not be as sharp as those produced with an adjustable focus, larger-aperture lens, again posing a disadvantage in a vision system application where sharply defined edges can be highly desirable. Moreover, it is sometimes desirable to distinguish between in-focus and out-of-focus areas on an object, which is problematic with conventional EDOF cameras.